Explore applications of artificial intelligence and machine learning tools in subseasonal-to-seasonal (S2S) climate predictions through this one-hour conference talk by Rajib Chattopadhyay. Discover how advanced AI/ML techniques are being applied to improve weather and climate forecasting capabilities on timescales ranging from two weeks to several months. Learn about the specific challenges in S2S prediction and how machine learning approaches can address limitations in traditional physics-based models. Understand the practical implementation of AI tools for enhancing prediction accuracy in this critical forecasting timeframe that bridges weather and climate scales. Gain insights into current research developments and methodologies being used to leverage artificial intelligence for better understanding and predicting Earth system variability on subseasonal to seasonal timescales.